In the last number of years deep learning models have made a significant impact across a range of fields. Machine Translation is one such area of research. The development of the encoder-decoder architecture and its extension to include an attention mechanism has led to deep learning models achieving state of the art MT results for a number of langauge pairs. However, an open question in deep learning for MT is what is the best attention mechanism to use. This talk will begin by reviewing the current state of the art in deep learning for MT. The second half of the talk will present a novel attention based encoder-decoder architecture for MT. This novel architecture is the result of collaborative research between John Kelleher, Giancarlo Salton, and Robert J. Ross.